规模化卫生系统数据:使翻译发现成为现实 (Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality)

Tiffany J. Callahan,Adrianne L. Stefanski,Jordan M. Wyrwa,Chenjie Zeng,Anna Ostropolets,Juan M. Banda,William A. Baumgartner Jr.,Richard D. Boyce,Elena Casiraghi,Ben D. Coleman,Janine H. Collins,Sara J. Deakyne-Davies,James A. Feinstein,Melissa A. Haendel,Asiyah Y. Lin,Blake Martin,Nicolas A. Matentzoglu,Daniella Meeker,Justin Reese,Jessica Sinclair,Sanya B. Taneja,Katy E. Trinkley,Nicole A. Vasilevsky,Andrew Williams,Xingman A. Zhang,Peter N. Robinson,Patrick Ryan,George Hripcsak,Tellen D. Bennett,Lawrence E. Hunter,Michael G. Kahn

from arxiv, Supplementary Material is included at the end of the manuscript

Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide semantically computable representations of biological knowledge and enable the integration of a variety of biomedical data. However, mapping EHR data to OBO Foundry ontologies requires significant manual curation and domain expertise. We introduce a framework for mapping Observational Medical Outcomes Partnership (OMOP) standard vocabularies to OBO Foundry ontologies. Using this framework, we produced mappings for 92,367 conditions, 8,615 drug ingredients, and 10,673 measurement results. Mapping accuracy was verified by domain experts and when examined across 24 hospitals, the mappings covered 99% of conditions and drug ingredients and 68% of measurements. Finally, we demonstrate that OMOP2OBO mappings can aid in the systematic identification of undiagnosed rare disease patients who might benefit from genetic testing.

翻译：共同数据模型解决了电子健康记录数据标准化的诸多挑战,但无法将深造所需的资源进行精密整合。开放生物和生物医学本体(OBO)铸造物学(OBO)提供了生物知识的生理可比较性可比较的表达方式,并能够将各种生物医学数据整合在一起。然而,将EHR数据绘制到OBO铸造物学(EHR)中,需要大量手工整理和领域专门知识。我们引入了观察医学成果伙伴关系标准词汇表(OMOP)用于OBO铸造物学的绘图框架。我们利用这个框架,为92,367个条件、8,615个药物成分和10,673个测量结果绘制了地图。测绘准确性得到了域专家的核实,在对24家医院进行检查时,地图覆盖了99%的条件和药物成分以及68%的测量结果。最后,我们证明OMOPOOBO的绘图有助于系统识别可能受益于基因测试的未诊断的稀有病的病人。